01
Run a project
Carry goals, files, decisions, tools, and next actions across sessions instead of rebuilding context every time.
Local-first composable intelligence
Lokai
Intelligence that works as a system.
One private operating layer coordinates the models, tools, memory, workflows, and hardware needed to move your work forward. You describe the outcome. Lokai handles the system beneath it and keeps consequential actions bound to your authority.
Public launch candidate in validation
What is Lokai?
A partner for the work, not another tab to manage.
Most AI products begin and end with a chat window. Lokai is the operating layer underneath the conversation. It can find the right capability, bring in grounded context, run local or optional external models, use approved tools, preserve project state, and leave a receipt of what happened.
Useful by design
Start with what you need done.
Lokai keeps technical choices beneath a clear working surface. The system can use one model or compose several capabilities when the task needs more depth.
02
Unify a workflow
Operate creator or business tools around the work itself, with provider plugins appearing only when they add value.
03
Use your own hardware
Coordinate a laptop, workstation, server, phone, or LAN workers while keeping authority and state with you.
04
Keep a trusted record
See the decisions, tool actions, runtime evidence, and approvals that matter without exposing internal noise.
How it works
One request. A governed path to an outcome.
The architecture stays out of your way, but it remains visible enough to earn trust. Lokai separates deterministic control from model generation so authority does not depend on a model remembering the rules.
-
1
Understand and route
Your goal enters a deterministic control plane.
Lokai selects relevant context, available capabilities, hardware, policy, and authority before execution begins.
-
2
Compose and work
The smallest useful set of intelligence goes to work.
A local model, a mixture of models, tools, skills, or optional cloud capacity can be composed around the task.
-
3
Verify and remember
Results return with state, evidence, and operator control.
Lokai checks the outcome, records the useful trace, asks when authority is needed, and carries the project forward.
Your system, your choices
Local-first does not mean local-only.
Keep private work on hardware you control. Add LAN workers when another machine can help. Use a cloud model only when you choose it and the task boundary allows it. Continue in a familiar agent harness while Lokai provides governance, runtime, memory, and receipts underneath.
DataPrivate by default
ModelsLocal, LAN, or optional cloud
InterfacesLokai or a compatible harness
GrowthLocal unless you opt in to share
AuthoritySet by the operator
Current proof
A working private system nearing public launch.
This is an engineering checkpoint, not a claim that every model, backend, or device is finished. Public claims will stay tied to named validation and performance receipts.
Working now
- Deterministic routing, grounding, authority, and receipt paths
- Optimized Rust-owned streaming execution for supported model lanes
- One-model and multi-lineage execution under the same control plane
- Durable project sessions, capability routing, plugins, and LAN workers
- Public-stack build, validation, setup, model-pack, and worker surfaces
- Protected compiled runtime artifacts for qualified Windows and Linux targets
Measured agent repair
Preserve the evidence. Stop paying to resend it.
A persistent-agent handoff repair kept tool evidence available across warm continuation and recovery while removing repeated prompt work on one frozen test shape.
- Warm request wall
- 33.322s to 15.989s
- Prompt tokens
- ~3,577 to 1,279
- Measured reduction
- 52.0%
Exact-shape transport result, not a universal speed claim or proof of task completion. Model outputs differed, and the longer engineering task remained unresolved.
Performance is model, format, backend, hardware, batch, and workload specific. Lokai publishes evidence by exact run shape.
An extensible foundation
Use the system. Extend the parts your deployment needs.
Lokai is not designed around one frozen model or one vendor's hardware. Public interfaces, typed contracts, modular runtime components, and evidence gates let developers and local agents add support without receiving protected routing internals or bypassing verification.
01 / Runtime
Add hardware and backend support.
Build against stable ABI and SDK boundaries, qualify the real device path, and publish exact support receipts. Runtime selection remains hardware-aware rather than forcing one configuration everywhere.
02 / Models
Add models, quants, and MLF packs.
Index model tensors into the MLF container, declare architecture and quantization contracts, test correctness, then optimize the modular lane. Mixed-precision and custom-quant experiments stay bound to the model and workload evidence that qualified them.
03 / Learning
Adapt from real, opted-in work.
Turn reviewed workflow traces and local corpora into bounded distillation, adapter, grounding, and precision experiments. Candidates remain inspectable and reversible until held-out evaluation earns promotion.
04 / Community
Share one capability, not your deployment.
Opt in at the artifact level: a skill, plugin, adapter, model contract, backend, or optimization can enter verification without exposing unrelated files, private memory, or deployment secrets.
Extension surfaces are real; support remains qualified per artifact, model, quant, backend, operating system, device, and workload. A submitted candidate is not automatically trusted or promoted.
Deployment services
Start with one measurable outcome on infrastructure you control.
Lokai is accepting conversations for private pilot and engineering work before public GA. Every scope is tied to named data, hardware, models, workflows, acceptance evidence, and explicit exclusions.
01 / Diagnose
Production RAG and AI-agent reliability
Reproduce retrieval, context, tool-use, evaluation, observability, or deployment failures before adding more prompts and framework layers.
Outcome: a measured baseline, prioritized repair plan, and one bounded candidate comparison.
02 / Deploy
Private AI architecture and local deployment
Qualify the hardware, model, grounding, storage, and network path, then stand up one customer-owned pilot with explicit authority and receipt boundaries.
Outcome: an operator-accepted local pilot with a tested recovery path.
03 / Extend
Governed workflow and model integration
Connect one real workflow, grounding source, model lane, adapter experiment, backend, or hardware target through typed actions and truthful execution receipts.
Outcome: a tested extension with explicit pass, fail, fallback, and rollback behavior.
Discovery first
Tell us what is failing, what must stay private, and what evidence would prove success.
Availability is scoped engagement by engagement. This is not a claim of universal model, backend, device, cluster, autonomous-completion, or performance support.
Access without artificial limits
One full public stack. One complete custom deployment.
Source protection and capability access are different decisions. Lokai can ship protected implementation as verified compiled artifacts without withholding the capability from the people using it.
Public / freeware
The full released capability set.
A useful, non-degraded local stack with supported runtime capabilities, public model-pack workflows, plugins, workers, memory, governance, and setup. No paid feature tiers. No expansion purchases.
Custom / enterprise
One licensed deployment, fully equipped.
One agreed price covers every released Lokai capability that adds value to the deployment. Commercial rights, private integration, private packs, data mapping, white-label work, validation, support, and service scope are what make the engagement custom.
There is no stronger hidden product reserved for enterprise.
Custom deployments are deeper fits of the same system, not a paywall around its intelligence.
Origin
Built by describing the system that needed to exist, then proving it one seam at a time.
Lokai was created and architected by Mohammad Teimoortagh Jr. through a human-directed, AI-assisted engineering process. The work grew from an idea into a measurable system by defining behavior, building a baseline, checking coherence and parity, and keeping improvements that survived proof.
Lokai Sovereign LLC owns and stewards the assigned Lokai business IP. The north star remains simple: Home is sanctuary.
FAQ
Direct answers before launch.
Does my data have to leave my hardware?
No. Lokai is local-first, and private user state stays local by default. External services or cloud models are optional choices with their own explicit boundaries.
Does Lokai include one required model?
No. Models are optional packs selected for the task, hardware, and upstream license. Lokai can operate one model, compose multiple models, or route work to another trusted worker.
Can I use a cloud model?
Yes, when you choose to connect one. Local-first is an ownership posture, not a ban on useful external capacity.
Will the public stack cost money to unlock capabilities?
No. The intended public/freeware stack is fully capable and non-degraded. Optional third-party services, models, and hardware can still carry their own costs and licenses.
Is Lokai open source?
The intended launch posture is freeware with protected compiled engine and routing implementations, not a fully open-source core. Public interfaces, tooling, docs, and approved components remain inspectable; protected source and private keys do not ship.
Do enterprise customers receive a stronger hidden version?
No. They receive a custom deployment of the same full system: commercial scope, private integrations, private data mapping, private packs, white-label work, validation, support, and service terms tailored to their environment.
Can I keep using an agent harness I already know?
That is the intended architecture. Lokai is being built to sit beneath compatible harnesses as the governance, memory, runtime, and auditability layer rather than forcing one interface on every user.
Can developers add hardware, models, or optimizations?
Yes. Lokai's modular runtime, stable extension boundaries, MLF model-pack workflow, plugins, and opt-in contribution path are designed for that work. Every addition still has to prove its exact compatibility and behavior; extension does not bypass qualification or expose protected ASR routing internals.
Launch access is being prepared
Bring useful intelligence home.
Join the launch conversation, evaluate a hardware or software partnership, or discuss a complete custom deployment.